Classifications

• • H—ELECTRICITY
  • H01—ELECTRIC ELEMENTS
  • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
  • H01M8/00—Fuel cells; Manufacture thereof
  • H01M8/18—Regenerative fuel cells, e.g. redox flow batteries or secondary fuel cells
  • H01M8/184—Regeneration by electrochemical means
  • H01M8/186—Regeneration by electrochemical means by electrolytic decomposition of the electrolytic solution or the formed water product
• • C—CHEMISTRY; METALLURGY
  • C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
  • C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
  • C25B1/00—Electrolytic production of inorganic compounds or non-metals
  • C25B1/01—Products
  • C25B1/24—Halogens or compounds thereof
  • C25B1/26—Chlorine; Compounds thereof
• • C—CHEMISTRY; METALLURGY
  • C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
  • C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
  • C25B15/00—Operating or servicing cells
  • C25B15/02—Process control or regulation
• • C—CHEMISTRY; METALLURGY
  • C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
  • C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
  • C25B9/00—Cells or assemblies of cells; Constructional parts of cells; Assemblies of constructional parts, e.g. electrode-diaphragm assemblies; Process-related cell features
  • C25B9/17—Cells comprising dimensionally-stable non-movable electrodes; Assemblies of constructional parts thereof
  • C25B9/19—Cells comprising dimensionally-stable non-movable electrodes; Assemblies of constructional parts thereof with diaphragms
• • H—ELECTRICITY
  • H01—ELECTRIC ELEMENTS
  • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
  • H01M10/00—Secondary cells; Manufacture thereof
  • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
• • H—ELECTRICITY
  • H01—ELECTRIC ELEMENTS
  • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
  • H01M10/00—Secondary cells; Manufacture thereof
  • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
  • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
  • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02E60/10—Energy storage using batteries
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
  • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02E60/30—Hydrogen technology
  • Y02E60/50—Fuel cells

Definitions

• the present invention relates to a method and system to characterize an electrolyser performance composed with elementary electrochemical cells used in an industrial scale process (Chlor-Alkali, Chlorate production plants and Fuel cells). More particularly, it relates to monitoring the electrochemical cell components by using curve fitting and estimating the overall electrolyzer performance by using predictive models.
• An electrolyzer is defined as an apparatus where an electrolysis reaction takes place. Electrolysis is the process of decomposing a chemical compound into its elements or producing a new compound by the action of an electrical current. Basically, an electrolyzer is composed of two electrodes and a separator called a membrane. In the Chlor-alkali industry, primary products of electrolysis are chlorine , hydrogen , and sodium hydroxide solution (commonly called “caustic soda” or simply “caustic”). Three main electrolysis processes are used in the Chlor-Alkali industry : membrane, diaphragm and mercury. Because of the growing environmental concerns the latter processes are being replaced the membrane electrolysis process.
• FIG. 1 identified as Prior Art is a schematic representation of a typical membrane cell used in the Chlor-alkali industry. It is composed of two compartments. The anode compartment is fed-up with a saturated brine solution (NaCI) while a dilute caustic soda passes through the cathode compartment. In the chlor-alkali plants, chlorine is generated at the coated (usually Ti) anode 2. The combination of hydroxide ions with migrated sodium ions across the selective membrane 1 generates caustic soda (NaOH) and Hydrogen gas. The cathode 3 is usually nickel with a catalytic coating to reduce the overpotential for H2 evolution. The complete chlor-alkali process is described by the following equation:
• an electrolyzer is defined as a combination of elementary membrane cells.
• the electrolysis process takes place in each cell after applying a current. Therefore, the electrolyzer energy consumption plays a key role in the process.
• the electrolyzer overall performance is mainly related to each cell efficiency. It is well known in the art ("A First course in Electrode Processes", Arthur Pletcher, "Ion Permeable Membranes”, Thomas A. Davis, J. David Genders, Derek Pletcher), that voltage variations in the membrane cell are generally a result of physical changes within the cell components.
• the cell voltage variation is distributed between its components: anode, cathode, membrane and electrical connections. An abnormal decrease or increase in the cell voltage is generally considered as a premise to potential problems.
• one aspect of the present invention is to propose analytical methods for the extraction of good curve fitting coefficients and a procedure for the classification of those parameters.
• R s Structure/contact resistance : Current density
• Schetter Thomas in patent application DE10217694 describes a method for dynamic determination of the voltage-current characteristic curve of a fuel cell during operation under different loading conditions. Although this document addresses the problem of extracting voltage-current linear curve parameters, it doesn't bring a useful method for analyzing these parameters in an industrial scale and relate them to cell performance.
• n Number of cells in the electrolyser F : Faraday constant
• the aim of one aspect of the present invention is the online generation of relationship between current efficiency and operational measurements such as the sodium hydroxide and catholyte temperature.
• One aspect of the present invention concerns a method for the characterization of each elementary cell by using analytical coefficients extracted from the application of curve fitting to current-voltage data, measured periodically and stored in a database.
• the generated analytical parameters are used as indicators to spotlight which part of the cell is the source of operation failing or loss of performance.
• a cell is highlighted as abnormal or failing throw comparing its characterization parameters with a reference cell or a known aging standards.
• a further aspect of this invention is related to the forecasting of the electrolyzer efficiency in an electrolysis process by using data-driven models based on the learning of the relationship between operational parameters and the calculated efficiency.
• FIG. 1 (Prior art) represents a typical diagram of a membrane cell used in the Chlor-Alkali process.
• FIG. 2 shows the structure of the elementary membrane cell analysis by diagnosing fitting coefficients.
• FIG. 3 is a typical start-up zone in an electrolyzer current.
• FIG. 4 is a typical shut down zone in an electrolyzer current.
• FIG. 5 is a typical load change zone in an electrolyzer.
• FIG.6 illustrates a nonlinear curve fitting with statistics (confidence bounds).
• FIG.7 shows an example of classifying fitting coefficients based on operation ranges.
• FIG.8 is a flow diagram for the electrolyzer efficiency optimization procedure
• FIG.9 is a flow diagram for the electrolyzer efficiency optimization procedure
• an electrolyzer is defined as a combination of elementary electrochemical cells.
• An elementary cell is defined as the smallest group of anodes and cathodes that are connected to the same current feeder and separated by a membrane. The way the anodes, cathodes and membrane are connected differ according to the used technology.
• the diagnosed electrolyzers are used in Chlor-alkali, Chlorate plants. Different aspects of the present invention could also be applied to fuel cells.
• FIG.2 illustrates the flow chart diagram for the characterization methodology based on curve fitting coefficients analysis addressed in the principal aspect of this invention.
• Historical Database unit 4 stores sufficient number of chronological data to characterize elementary membrane cells and electrolyzers. According to a preferred embodiment, the cell voltage and the current are measured through the system outlined in US patent No. 6,591 ,199 to Diego 2000 inc. Other relevant parameters measured by the plant sensors are imported into the database by using a communications protocol within the aforementioned platform. Due to storing and communication considerations, voltage, current and external parameters aren't acquired with the same time stamp. Therefore, the Extraction unit 5 synchronizes the data values from the different parameters to the same time stamp.
• the synchronization is jperformed by interpolating the missed data points through a piecewise linear interpolation.
• Other well-known techniques such as zero-holder filters, zero padding, spline interpolation, etc., might be used.
• the Extraction unit 5 selects suitable operation zones for the curve fitting analysis.
• Three types of cell operation zones are concerned with the method depicted in FIG.2: start-up zones, shutdown zones and load change zones. Basically, the current defined all the suitable operation zones.
• An example of a start-up zone is depicted in FIG. 3.
• electrolyzer current rises from low values to high values through stable steps.
• a shut down zone is represented by a fall in the current values from high to low by stable steps as illustrated in FIG. 4.
• a sequence of load change is also suitable for cell characterization by curve fitting analysis as illustrated in FIG. 5.
• data extraction in unit 5 is performed manually by the means of a user graphical interface or automatically by several analytical methods.
• One of the said methods performs the extraction of stable zones 1 1 12 13 from a driving parameter such as the electrolyzer current.
• stable zones are detected through scanning the driving parameter and appointing those that represent a statistical normal distribution with a slope close to zero.
• Another method performs similar stable zones extraction by spotlighting data sequences within the driving parameter variance range predefined by the user.
• Unit 6 filters voltage, current and external values from irrelevant data points. Ill-conditioned values are mainly missed or out-of-range data points resulting from drifting or disconnected sensors.
• the filtering in Unit 6 is performed by the mean of smoothing techniques in the time domain of signal processing techniques in frequency domain (windowing, wavelets etc).
• unit 6 also performs voltage standardization. This latter represents compensation to voltage variation due to operational parameters such as catholyte temperature and sodium hydroxide concentration.
• voltage standardization to operational parameters is performed by linear equations such as the following:
• the unit 7 After the filtering and standardization task is finished, the unit 7 performs the curve fitting operation on the selected current-voltage values for each elementary cell.
• the curve fitting is done through the application of a non-linear least square procedure on Equation 1.
• the non-linear least square could be applied to the current-voltage selected points or any tendency measure (mean, median etc.) on each stable zone.
• the extraction of the fitting coefficients uses the method of least squares when fitting the data.
• least squares method minimizes the summed square of residuals.
• the residual for the ith current-voltage data point ri is defined as the difference between the raw values Vi and the fitted values ⁇ , and is identified as the error associated with the data.
• n is the number of data points included in the fit and SSE is the sum of squares error estimate.
• R-square is the square of the correlation between the real values and the predicted values. It is also called the square of the multiple correlation coefficient and the coefficient of multiple determination.
• SSR is defined as the ratio of the sum of squares of the regression (SSR) and the total sum of squares (SST). SSR is defined as:
• SST is also called the sum of squares about the mean, and is defined as:
• R-square can take any value between 0 and 1 , with a value closer to 1 indicating a better fit.
• an R2 value of 0.8234 means that the fit explains 82.34% of the total variation in the data about the average.
• all the triplets that were generated with an R2 less than 0.99 are not taken in account for the next step of the process.
• Unit 8 calculates confidence bounds for the fitting triplet.
• Confidence bounds define the lower and upper values of the associated coefficient, and define the width of the interval. This width of interval indicates uncertainty about the fitted coefficients, the predicted observation, or the predicted fit. For example, a very wide interval for the fitted coefficients indicates that we should use more data when fitting before we can say anything very definite about the coefficients.
• the bounds are defined with a level of certainty that is specified. The level of certainty is often 95%, but it can be any value such as 90%, 99%, 99.9%, and so on. For example, we might want to take a 5% chance of being incorrect about predicting a new observation. Therefore, we would calculate a 95% prediction interval. This interval indicates that we have a 95% chance that the new observation is actually contained within the lower and upper prediction bounds.
• FIG. 6 depicts an example of confidence bounds for a nonlinear curve fitting of Equation 1.
• Fitting coefficients with good confidence are used as a reference to the final characterization operation.
• Unit 9 applies a classification or grouping procedure on each generated fitting parameters for the elementary cell.
• the aim of the grouping process is to devise Ihe range of each fitting coefficients into classes of operation.
• the move of a fitting parameter from one class to another is an indicator of potential premature aging or loss of performance.
• Coefficients membership to operation classes could also be analyzed with respect to the elementary cell components (membrane, anode and cathode).
• the classification procedure is applied on a coefficient reference range defined by the plant expert or whether based on an operation zone defined as a reference.
• a reference coefficient is extracted from a cell called "Reference Cell", this latter is iknown as new or good performing cell compared to the others with the same or equivalent components.
• FIG. 7 illustrates the evolution of the coefficient UO for an elementary cell where 3 operation classes were defined: class 1 "new coating” 16, class 2 "old coating” 15, class 3 "No coating” 14. Furthermore, the UO drift from class 1 to class 3 clearly indicates that this cell is bsing its electrodes coating.
• FIG. 8 Another example is illustrated by FIG. 8 where we can deduce the behaviour of the cell in the electrolyser by comparing their UO values to those of cell 8 (reference cell).
• fuzzy C means clustering algorithm. According to this algorithm the user must define the desired number of groups in the reference fitting coefficient range, then the membership degree of new presented values is defined with respect to the predefined reference groups.
• Another way to perform the characterization parameters monitoring is to track their deviation against a predefined aging model (related to the cell components technology).
• FIG. 9 describes another aspect of the present invention.
• the flow chart diagram illustrates the methodology for the electrolyzer overall performance evaluation and diagnosis based on its efficiency.
• the diagnosis process is made by means of two phases: learning phase and deployment phase.
• learning phase the acquired data from the electrolyzer are stored in a historical database Unit 17.
• Two categories of data are imported from the database, real time recording such as cell voltage, current, flows etc. and punctual measurements such as electrolyzer inlet and outlet pH, impurities deposits etc.
• Unit 18 selects from the steady state operation zones based on a driving parameter like the electrolyzer current. It also filters the ill-conditioned data points due to the shutdowns, disconnected or drifted sensors. Since the data came from different sources with several acquisition time stamps, Unit 18 performs also the synchronization process, which generates data series with the same time stamp.
• Unit 19 calculates the current or production efficiency for the electrolyzer. Production or current efficiency calculation depends on used cell technology, products generated and control objectives. As an example, for chlor-alkali plants the efficiency could be calculated for one or two products (cathode or anode part). Formally, the efficiency is defined as the ratio of the produced species by the theoretical production (based on the current consumption). While the mass balance plays a key role in the efficiency calculation it is important to filter the ill conditioned measurements through unit 18. According to a preferred embodiment, unit 20 generates operation parameters values (Caustic soda outlet concentration, catholyte temperature as an example) that maximize the obtained current efficiency values with unit 18 on a relatively little time period (days or weeks).
• operation parameters values (Caustic soda outlet concentration, catholyte temperature as an example) that maximize the obtained current efficiency values with unit 18 on a relatively little time period (days or weeks).
• the generated then operation values are tested on the field plant 21 for the validation purpose. If the obtained performance is judged sufficient, the values will be used as reference to generate the forecasting model with unit 22. Predictive models relate the maximum efficiency values to the operation parameters by using parametric or non- parametric modeling techniques such as neural networks. Finally the generated models and preprocessing actions are stored in the Characterization Knowledgebase unit 23. In the deployment phase, electrolyzer efficiency is predicted by applying reference model 25 to the acquired and processed raw data 24. Based on that prediction, preventive control actions (adjusting setups, thresholds etc.) are better planned.

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